def test_custom_video():
from brainrender.animation.video import CustomVideoMaker
# --------------------------------- Variables -------------------------------- #
N_FRAMES = 20
# Variables to specify camera position at each frame
zoom = np.linspace(1, 1.35, N_FRAMES)
frac = np.zeros_like(
zoom
) # for camera transition, interpolation value between cameras
frac[:10] = np.linspace(0, 1, 10)
frac[10:] = np.linspace(1, 0, len(frac[10:]))
# ------------------------------- Create scene ------------------------------- #
scene = Scene(display_inset=True, use_default_key_bindings=True)
filepaths, data = scene.atlas.download_streamlines_for_region("TH")
scene.add_brain_regions(["TH"], alpha=0.2)
# Create new cameras
cam1 = buildcam(sagittal_camera)
cam2 = buildcam(top_camera)
cam3 = buildcam(
dict(
position=[1862.135, -4020.792, -36292.348],
focal=[6587.835, 3849.085, 5688.164],
viewup=[0.185, -0.97, 0.161],
distance=42972.44,
clipping=[29629.503, 59872.10],
)
)
# Iniziale camera position
scene.plotter.moveCamera(cam1, cam2, frac[0])
# ------------------------------- Create frames ------------------------------ #
def frame_maker(scene=None, video=None, videomaker=None):
for step in track(
np.arange(N_FRAMES),
total=N_FRAMES,
description="Generating frames...",
):
# Move scene camera between 3 cameras
if step < 150:
scene.plotter.moveCamera(cam1, cam2, frac[step])
else:
scene.plotter.moveCamera(cam3, cam2, frac[step])
# Add frame to video
scene.render(zoom=zoom[step], interactive=False, video=True)
video.addFrame()
return video
# ---------------------------------------------------------------------------- #
# Video maker #
# ---------------------------------------------------------------------------- #
vm = CustomVideoMaker(scene, save_name="streamlines_animation")
vm.make_video(frame_maker)
def test_animated_scene():
# --------------------------------- Variables -------------------------------- #
minalpha = 0.01 # transparency of background neurons
darkcolor = "lightgray" # background neurons color
N_FRAMES = 50
N_neurons = 4 # number of neurons to show in total, if -1 all neurons are shown but it might take a while to render them at first
N_neurons_in_frame = (
2 # number of neurons to be highlighted in a given frame
)
N_frames_for_change = 15 # every N frames which neurons are shown changes
# Variables to specify camera position at each frame
zoom = np.linspace(1, 1.5, N_FRAMES)
frac = np.zeros_like(
zoom) # for camera transition, interpolation value between cameras
frac[:10] = np.linspace(0, 1, 10)
frac[10:] = np.linspace(1, 0, len(frac[10:]))
# -------------------------------- Fetch data -------------------------------- #
# Then we can download the files and save them as a .json file
ml_api = MouseLightAPI()
# Fetch metadata for neurons with some in the secondary motor cortex
neurons_metadata = ml_api.fetch_neurons_metadata(filterby="soma",
filter_regions=["MOs"])
neurons_files = ml_api.download_neurons(neurons_metadata[:N_neurons])
# ------------------------------- Create scene ------------------------------- #
scene = Scene(display_inset=False, use_default_key_bindings=True)
neurons_actors = scene.add_neurons(neurons_files,
neurite_radius=12,
alpha=0)
# Create new cameras
cam1 = buildcam(sagittal_camera)
cam2 = buildcam(
dict(
position=[-16624.081, -33431.408, 33527.412],
focal=[6587.835, 3849.085, 5688.164],
viewup=[0.634, -0.676, -0.376],
distance=51996.653,
clipping=[34765.671, 73812.327],
))
cam3 = buildcam(
dict(
position=[1862.135, -4020.792, -36292.348],
focal=[6587.835, 3849.085, 5688.164],
viewup=[0.185, -0.97, 0.161],
distance=42972.44,
clipping=[29629.503, 59872.10],
))
# ------------------------------- Create frames ------------------------------ #
# Create frames
prev_neurons = []
for step in track(np.arange(N_FRAMES),
total=N_FRAMES,
description="Generating frames..."):
if step % N_frames_for_change == 0: # change neurons every N framse
# reset neurons from previous set of neurons
for neuron in prev_neurons:
for component, actor in neuron.items():
actor.alpha(minalpha)
actor.color(darkcolor)
prev_neurons = []
# highlight new neurons
neurons = choices(neurons_actors, k=N_neurons_in_frame)
for n, neuron in enumerate(neurons):
color = colorMap(n,
"Greens_r",
vmin=-2,
vmax=N_neurons_in_frame + 3)
for component, actor in neuron.items():
actor.alpha(1)
actor.color(color)
prev_neurons.append(neuron)
# Move scene camera between 3 cameras
scene.plotter.moveCamera(cam1, cam2, frac[step])
if frac[step] == 1:
cam1 = cam3
# Update rendered window
time.sleep(0.1)
scene.render(zoom=zoom[step], interactive=False, video=True)
scene.close()
# ------------------------------- Create scene ------------------------------- #
scene = Scene(display_inset=True, use_default_key_bindings=True)
root = scene.actors['root']
filepaths, data = scene.atlas.download_streamlines_for_region("TH")
scene.add_streamlines(data, color="darkseagreen", show_injection_site=False)
scene.add_brain_regions(['TH'], alpha=.2)
# Make all streamlines background
for mesh in scene.actors['tracts']:
mesh.alpha(minalpha)
mesh.color(darkcolor)
# Create new cameras
cam1 = buildcam(sagittal_camera)
cam2 = buildcam(top_camera)
cam3 = buildcam(
dict(
position=[1862.135, -4020.792, -36292.348],
focal=[6587.835, 3849.085, 5688.164],
viewup=[0.185, -0.97, 0.161],
distance=42972.44,
clipping=[29629.503, 59872.10],
))
# Iniziale camera position
startcam = scene.plotter.moveCamera(cam1, cam2, frac[0])
neurons_files = ml_api.download_neurons(neurons_metadata[:N_neurons])
# ------------------------------- Create scene ------------------------------- #
scene = Scene(display_inset=False, use_default_key_bindings=True)
neurons = scene.add_neurons(neurons_files, neurite_radius=12, alpha=0)
# Make all neurons background
for neuron in neurons:
for mesh in neuron.values():
mesh.alpha(minalpha)
mesh.color(darkcolor)
# Create new cameras
cam1 = buildcam(sagittal_camera)
cam2 = buildcam(
dict(
position=[-16624.081, -33431.408, 33527.412],
focal=[6587.835, 3849.085, 5688.164],
viewup=[0.634, -0.676, -0.376],
distance=51996.653,
clipping=[34765.671, 73812.327],
))
cam3 = buildcam(
dict(
position=[1862.135, -4020.792, -36292.348],
focal=[6587.835, 3849.085, 5688.164],
viewup=[0.185, -0.97, 0.161],